1. Field of Invention
This invention relates to the field of positron emission tomography (PET). More specifically, the present invention is related to a device and method for improving PET resolution through on-line correction of patient motion.
2. Description of the Related Art
In the art of PET detection, it is known that the resolution of a scan is in part dependent upon the amount of motion of the patient during the scan, and specifically the motion of the portion of the body being scanned. It is known that typical PET scans last from fifteen (15) minutes to three (3) hours. Ideally, if the patient could lie motionless during a PET scan, a high resolution PET system such as the CTI Pet Systems, Inc., EXACT HR+ is capable of 4 to 5 mm 3-D image resolution. However, if the patient or object in the field of view (FOV) does not remain stationary, the PET image is blurred. Practically speaking, inadvertent or disease-induced patient motion over the course of the scan currently blurs the 4 to 5 mm resolution to a far less effective 10 to 15 mm or worse.
Recent work has shown that techniques exist for tracking patient motion during the PET scan. B. J. Lopresti et al., “Implementation and Performance of an Optical Motion Tracking System for High Resolution Brain PET Imaging,” IEEE Trans. Nucl. Sci., Vol. 46, No. 6, 2059–2067 (Dec. 1999), shows effective tracking of patient head motion to all 6 degrees of 3-D movement. Specifically, they show tracking of the X, Y, and Z offsets to an accuracy of fractions of a millimeter, as well as yaw, pitch, and roll to acceptable angular accuracy. Encouraged by Lopresti's effort, the industry is currently moving toward both partial and more complete analytical solutions to the blurring problem. Partial solutions include gating the PET data into time slices during which the patient is shown to be more stationary. In more complete solutions the measured head position is applied to small time slice groups of PET data or even grouped with individual detector-pair event PET data in list mode form. These solutions each apply a correction for patient motion after the PET scan has completed. The most likely application of the more complete correction is a list mode approach in which all the raw PET data is collected in a computer disk file. After the PET scan completes the data is reprocessed. While this event-by-event list mode approach corrects for the motion of, for example, a patient's head, the reprocessing of the file data is a time consuming task.
In clinical PET applications, economic pressures demand high patient throughput without compromise to ease of use. The aforementioned list mode approach to motion de-blurring is problematic in view of these economic pressures in that more operator interaction and unacceptably long processing times are required. Either the data that is stored for reprocessing must be moved to a separate processor, or the data is reprocessed on the computer used for scanning. For a typical scan of 30–45 minute duration, reprocessing the data requires 30 minutes to 3 hours of scanner time (equivalent to approximately one to six additional scans) or an independent computing system. Taking this further, if the PET scanner is to be used almost continuously for scanning only, it will be seen that several independent computing systems are required to reprocess the data collected in a most time-efficient manner.
Typically, with respect to PET, the skull and brain are considered a single, rigid object. However, whole body PET scans also suffer from a lack of correction for motion. While problems limit effectiveness compared to brain scans, gross motions of the whole human body may also be de-blurred. PET cardiac gating is one example of a well-known but crude method to compensate for the motion of the heart during a PET scan. Difficult technical challenges remain to more effectively correct for the relative motions between and within individual organs such as, among others, the heart, lungs, liver, and lymph nodes.
Another known obstacle to on-line motion correction in the context of most existing PET applications is on-line normalization. In standard PET, on-line histogramming permits an accumulating tally of true events to be recorded in computer memory bins. One bin is typically reserved for each line of response (LOR) or small group of LORs. Histogramming adds (or subtracts) unity to a designated memory bin for each prompt (or delayed) event. Normalization serves to compensate for variations in detector pair efficiency by scaling the bin values. This scaling is typically applied after histogramming completes and is almost never applied in any on-line or real-time fashion. To apply normalization in real time for typical human PET requires somewhat more complex electronic systems than are in typical use today. These on-line systems must rapidly add or subtract scalar values instead of unity.
Mathematical techniques for transformation from one 3-D coordinate system to another are well known. M. Spiegel, Schaum's Mathematical Handbook, McGraw Hill, p. 49 (1968), lists equations for translation and rotation from one 3-D coordinate system (x, y, z) to another (x′, y′, z′). Representative equations are as follows:x′=dxx*x+dxy*y+dxz*z+X  (1)y′=dyx*x+dyy*y+dyz*z+Y  (2)z′=dzx*x+dzy*y+dzz*z+Z  (3)where:                X, Y, and Z are translational offsets from the coordinate system (x, y, z) to the coordinate system (x′, y′, z′);        dxx, dxy, and dxz are direction cosines between the x-, y-, and z-axes and the x′ axis, respectively;        dyx, dyy, and dyz are direction cosines between the x-, y-, and z-axes and the y′ axis, respectively; and        dzx, dzy, and dzz are direction cosines between the x-, y-, and z-axes and the z′ axis, respectively.        
These three equations require nine multiplication operations and three operations involving the addition of four input variables.
The known digital electronic technique of pipelining is applied to arithmetic operations such that the speed of the electrical circuit is limited not by the whole computation but instead by the slowest individual piece of the computation. See Introduction to Computer Architecture by H. S. Stone, 1975, page 386, Section 9.3, “Pipelining as a Design Principle”. However, such technique has not been shown in the prior art to be successful in the environment of on-line correction of patient data in a PET scan to account for patient motion.